Abstract
Plant diseases, such as the pinewood disease, PWD, have become a problem of economical and forestall huge proportions. These diseases, that are asymptomatic and characterized by a fast spread, have no cure developed to date. Besides, there are no technical means to diagnose the disease in situ, without causing tree damage, and help to assist the forest management. Herein is proposed a portable and non-damage system, based on electrical impedance spectroscopy, EIS, for biological applications. In fact, EIS has been proving efficacy and utility in wide range of areas. However, although commercial equipment is available, it is expensive and unfeasible for in vivo and in field applications. The developed EIS system is able to perform AC current or voltage scans, within a selectable frequency range, and its effectiveness in assessing pine decay was proven. The procedure and the results obtained for a population of 24 young pine trees (Pinus pinaster Aiton) are presented. Pine trees were kept in a controlled environment and were inoculated with the nematode (Bursaphelenchus xylophilus Nickle), that causes the PWD, and also with bark beetles (Tomicus destruens Wollaston). The obtained results may constitute a first innovative approach to the diagnosis of such types of diseases.

Abstract
Geophysical experts aimed to establish a method to identify contamination by landfill leakage without chemically analyzing subsoil samples, which is time-consuming and expensive. To that end, researchers developed a software package that let the experts create 3D visualizations of geophysical data acquired around the landfill and apply statistical analysis to detect anomalous values. The data used, electrical resistivity, are typically sparse. So, the application employs kriging to interpolate the data and provide a volumetric representation of the subsoil resistivity. To avoid invalid conclusions, the visualization also represents uncertainty. The application enabled the experts to better understand the phenomenon and to develop and validate their method. Their evaluation of the application indicated that it helped them throughout the method's development and significantly eased their workload. © 2014 IEEE.

Abstract
Support for multiple concurrent applications is an important enabler for promoting the use of sensor networks as an infrastructure technology, where multiple users can deploy their applications independently. In such a scenario, different applications on a node may transmit packets at distinct periods, causing the node to change from sleep to active state more often, which negatively impacts the energy consumption of the whole network. In this paper, we propose to batch the transmissions together by defining a harmonizing period to align the transmissions from multiple applications at periodic boundaries. This harmonizing period is then leveraged to design a protocol that coordinates the transmissions across nodes and provides real-time guarantees in a multi-hop network. This protocol, which we call Network-Harmonized Scheduling (NHS), takes advantage of the periodicity introduced to assign offsets to nodes at different hop-levels such that collisions are always avoided, and deterministic behavior is enforced. NHS is a light-weight and distributed protocol that does not require any global state-keeping mechanism. We implemented NHS on the Contiki operating system and show how it can achieve a duty-cycle comparable to an ideal TDMA approach.

Abstract
In this paper it is described the prototyping of an instrumented chair that allows to fully-automate the "Timed Up and Go", the "30-Second Chair Stand" and the "Hand-Force "tests assessment. The presented functional chair prototype is a low cost approach that uses inexpensive sensors and the Arduino platform as the data acquisition board, with its software developed in LabVIEW. The "Timed up and go test" consists in measuring the time spent in the task execution of standing up from a chair, walk three meters with a maximum speed without running, turn a cone and going back to the initial position. The "30-Second Chair Stand" test consists in counting the number of completed chair stands in 30 seconds. It are agility, strength and endurance tests easy to setup and execute although they lack of repeatability, whenever the measures are taken manually, due to the rough errors that are introduced. The "Hand-Force" test consists in measuring the hand strength, the relevant data are the peak and average values of several tests. The referred data is important in order to evaluate hand rehabilitation treatment results.

Abstract
Setup operations are significant in some production environments. It is mandatory that their production plans consider some features, as setup state conservation across periods through setup carryover and crossover. The modelling of setup crossover allows more flexible decisions and is essential for problems with long setup times. This paper proposes two models for the capacitated lot-sizing problem with backlogging and setup carryover and crossover. The first is in line with other models from the literature, whereas the second considers a disaggregated setup variable, which tracks the starting and completion times of the setup operation. This innovative approach permits a more compact formulation. Computational results show that the proposed models have outperformed other state-of-the-art formulation.

Abstract
To cope with today market challenges and guarantee adequate competitive performances, companies have been decreasing their products life cycles, as well as increasing the number of product varieties and respective services available on their portfolio. Consequently, it has been observed an increasing in complexity in all domains, from product and process development, factory and production planning to factory operation and management. This reality implies that organizations should be able to compile and analyze, in a more agile way, the immense quantity of data generated, as well as apply the suitable tools that, based on this knowledge, will supports stakeholders to take decision envisioning future performance scenarios. Aiming to pursuing this vision was developed a proactive performance management framework, composed by a performance thinking methodology and a performance estimation engine. While the methodology developed is an extension of the Systems Dynamics approach for complex systems' performance management, on the other hand, the performance estimation engine is an innovative IT solution responsible by capturing lagging indicators, as well as estimate future performance behaviors. As main outcome of this research work, it was demonstrated that following a systematic and formal approach, it is possible to identify the feedback loops and respective endogenous and exogenous variables responsible by hindering the systems behavior, in terms of a specific KPI. Moreover, based on this enhanced understanding about manufacturing systems behavior, it was proved to be possible to estimate with high levels of confidence not only the present but also future performance behavior. From the combination of both qualitative and quantitative approaches, it was explored an enhanced learning machine algorithm capable to specify the curve of behavior, characteristic from a specific manufacturing system, and thus estimate future behaviors based on a set of leading indicators. In order to achieve these objectives, both Neural Networks and Unscented Kalman Filter for nonlinear estimation were applied. Important results and conclusions were extracted from an application case performed within a real automotive plant, which demonstrated the feasibility of this research towards a more proactive management approach.